Mathematical relationships between control group variability and assay quality metrics

IF 2.7 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS SLAS Discovery Pub Date : 2023-07-01 DOI:10.1016/j.slasd.2023.02.003
Andrew Lim
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Abstract

Assay quality metrics have been used in various high-throughput screening (HTS) campaigns to indicate assay quality. Z’-factor has become one of the most widely used metrics, along with other metrics such as standardised mean difference (SSMD). In using these metrics, it is important to understand how these metrics can be impacted by the separation between control groups (indicated by the HZ ratio) and the coefficient of variation (CV) within each control group. In this paper, several mathematical equations have been derived to understand the relationship between assay quality metrics (such as Z’-factor and SSMD) and control group datasets (summarised by CV and HZ). These equations increase our understanding of the factors that improve assay quality metrics, thus providing a quantitative means to visualise how affecting control groups can impact assay quality metrics.

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对照组变异性与测定质量指标之间的数学关系
化验质量指标已用于各种高通量筛选(HTS)活动,以指示化验质量。Z’因子与标准化平均差(SSMD)等其他指标一起,已成为最广泛使用的指标之一。在使用这些指标时,重要的是要了解这些指标如何受到对照组之间的分离(由HZ比率表示)和每个对照组内的变异系数(CV)的影响。在本文中,推导了几个数学方程来理解分析质量指标(如Z’-因子和SSMD)与对照组数据集(用CV和HZ总结)之间的关系。这些方程增加了我们对提高测定质量指标的因素的理解,从而提供了一种定量方法来可视化影响对照组如何影响测定质量指标。
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来源期刊
SLAS Discovery
SLAS Discovery Chemistry-Analytical Chemistry
CiteScore
7.00
自引率
3.20%
发文量
58
审稿时长
39 days
期刊介绍: Advancing Life Sciences R&D: SLAS Discovery reports how scientists develop and utilize novel technologies and/or approaches to provide and characterize chemical and biological tools to understand and treat human disease. SLAS Discovery is a peer-reviewed journal that publishes scientific reports that enable and improve target validation, evaluate current drug discovery technologies, provide novel research tools, and incorporate research approaches that enhance depth of knowledge and drug discovery success. SLAS Discovery emphasizes scientific and technical advances in target identification/validation (including chemical probes, RNA silencing, gene editing technologies); biomarker discovery; assay development; virtual, medium- or high-throughput screening (biochemical and biological, biophysical, phenotypic, toxicological, ADME); lead generation/optimization; chemical biology; and informatics (data analysis, image analysis, statistics, bio- and chemo-informatics). Review articles on target biology, new paradigms in drug discovery and advances in drug discovery technologies. SLAS Discovery is of particular interest to those involved in analytical chemistry, applied microbiology, automation, biochemistry, bioengineering, biomedical optics, biotechnology, bioinformatics, cell biology, DNA science and technology, genetics, information technology, medicinal chemistry, molecular biology, natural products chemistry, organic chemistry, pharmacology, spectroscopy, and toxicology. SLAS Discovery is a member of the Committee on Publication Ethics (COPE) and was published previously (1996-2016) as the Journal of Biomolecular Screening (JBS).
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